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Quantitative image analysis of thrombus formation in microfluidic in-vitro models
Micro and Nano Systems Letters volume 10, Article number: 23 (2022)
In this study, we present a method to quantitatively analyze the thrombus formation process through image analysis in an in vitro thrombus model with a circular cross section. The thrombus model used was designed based on the mechanism between the physical principle of wall shear rate (WSR) and thrombus formation. Image analysis was used to help visualize the thrombus formation process and calculate the thrombus area. Through this method, the thrombus formation and growth from the channel wall was demonstrated without the use of fluorescence. In addition, by dividing the image into sub-sections, the accuracy of the thrombus growth pattern was improved. The departing blood clots which are called embolus, were observed being separated from the thrombus.
A thrombus refers to a lump of blood in the blood vessels, and thrombosis is a condition in which a blood vessel is blocked by blood clots. Thrombosis shows various symptoms depending on the location of the organ and type of blood vessel. Generally, ischemia occurs in arterial thrombosis , and congestion is typical in the case of venous thrombosis . Thrombosis is caused by slow blood flow, excessive coagulation, and vascular damage, each of which acts alone or in combination to form a thrombus. Thrombus formation is affected by the physical shear rate, which increases the platelet concentration near the vessel wall and activates platelets in that area . Therefore, it is important to observe and analyze the relationship between thrombus formation and the wall shear rate (WSR) –. Particularly, there is a medical need for a rapid assay to evaluate thrombotic risk; antithrombotic therapy performed in arterial blood flow conditions with non-anticoagulated blood and point of care (POC) devices are necessary to quickly monitor the risk of arterial thrombosis and optimize in vitro antithrombotic therapy . To accurately detect thrombi, previous studies have used fluorescent substances such as DiOC6 to improve platelet visibility . However, platelet detection using fluorescent materials requires a special microscope capable of detecting fluorescence and affects physical properties, such as blood concentration and blood viscosity, which can increase the total experimental time . In this study, through image analysis, which is a post-processing process, the visibility of the thrombus was improved, growth process of the thrombus was observed, and area of the thrombus was quantitatively calculated, compared, and analyzed. This analytical method was used to describe the formation process of thrombi grown by WSR in in vitro thrombus models with different stenosis percentages. It is expected that manufacturing a highly reproducible shear rate-based thrombus model as a lab-on-a-chip can be applied to the development of shear-sensitive drugs, nanoparticles, and liposomes . Ultimately, this study aimed to provide insights into the development of new thrombosis propensity test systems .
The cross-section of the channel had a circular shape because the channel was formed using the thermal expansion principle . The principle of thermal expansion molding is to form a circular channel in partially cured polydimethylsiloxane (PDMS, SYLGARD 184, DOW, USA) by thermally expanding the air trapped inside the mold. The ratio of the stenosis section and cross-sectional profile of the microchannel was determined by controlling the curing temperature and curing time of the partially cured PDMS. Since this method uses heat for expansion and curing simultaneously, there is a limit in expansion for larger channels (> 200 μm) , and the cross-section has an elliptical shape rather than a perfect circle. However, since the cross-section is not rectangular, the advantage is that the WSR has a distribution similar to that of a human blood vessel . A 3D model and cross-sectional shape of a channel with 83.73% stenosis are shown in Fig. 1. The stenosis percentage was calculated using the hydraulic diameter of the channel. The formulas for calculating the hydraulic diameter and stenosis percentage were provided by Lui et al. 
where w and h are the lengths of the long and short sides of the channel, respectively. The hydraulic diameters calculated from the channel part (Fig. 1b) and stenosis part (Fig. 1c) were named Dh,channel and Dh,stenosis, respectively. The models used were PDMS channels with stenosis sections of 71.2% and 83.73%. To evaluate the relationship between the thrombus and WSR, the maximum WSR was calculated as follows:
where ϒ is the WSR, Q is the flow rate in the channel, and D is the channel diameter. Previous studies reported that the degree of platelet diffusion or aggregation switches around a WSR is 10,000 s−1 [15,16,17,18]. In this study, thrombus formation was expected to vary under conditions with a WSR of less than 10,000 s−1 and more than 10,000 s−1. Each channel has a WSR of 7602.36 s−1 for 71.2% stenosis and 29,850.27 s−1 for 83.73% stenosis when the blood flow was set to 3 mL/h. The maximum WSR was 10 s−1 for veins and 2000 for small arteries , and severe arterial stenosis occurs at a WSR of 5000 to 10,000 s−1. Therefore, the designed channels have a sufficient WSR to form a thrombus. The dimensions, stenosis percentage, and maximum WSR of the two channels used in this study are listed in Table 1.
Whole blood was collected in a citrate tube (2 mL 9NC Coagulation Sodium Citrate 3.2%; Greiner Bio-One GmbH, Austria) and used the same day. Citrate binds calcium ions to prevent them from interacting with the coagulation system. To reactivate the coagulation system interrupted by citrate  immediately before the experiment, whole blood was mixed with CaCl2 0.25 M (C1016-500G, Sigma Aldrich, China), maintained at 37 °C in a volume ratio of 10:1. The recalcified blood was perfused into the channel as soon as it was mixed.
To promote platelet coagulation at the PDMS channel surface, the channel was incubated with 1 mg/mL collagen (C3867-1VL, Type I solution from rat tail, Sigma Aldrich, South Korea) at 22 °C for more than 8 h, one day before the experiment. Before blood perfusion, the channels were rinsed with 1X phosphate-buffered saline (PBS, P5493-1L, Sigma Aldrich, USA) for 1 min to remove excess collagen. The channels were closely monitored for lumps or particles.
The experimental setup is illustrated in Fig. 2. Recalcified blood was perfused into the thrombus chip at 3 mL/h using a syringe pump (NE-4000 New Era Pump, USA), and the blood that passed through the chip was collected into an exhaust bottle. Blood clotting on the chip was observed in real time using a digital microscope (DMSZ7, SUNNY OPTICAL TECHNOLOGY, China). Images acquired with the microscope were transferred to a computer after the experiment and post-processed for image analysis.
Post-processing: image analysis
In this study, image analysis, a post-processing process, was used to visualize the process of thrombus formation and calculate the area of the thrombus. An open-source ImageJ software was used for this process. All the images had the same number of pixels (235 × 105 pixels).
Figure 3a shows an RGB image saved in a portable network graphics (PNG) format from the recorded images. The PNG format was chosen for the lossless compression. In the original image (Fig. 3a), there are limitations in identifying the thrombus region because it is colorless and changes shape dynamically .
In the first step, the overall tonal range was reduced (increased contrast), and the tonal value was increased (increased brightness). This highlights the thrombus area by clarifying the boundary between the object (thrombus) and the background .
In the second step, the image was converted into a grayscale image, as shown in Fig. 3c. This process is a preparation for the next step, that is, the binarization step. Since binarization is a process of converting an image with only two colors, white and black, the image must be converted into pixels with only a contrast ratio excluding the color information.
In the third step, the thrombus region was separated from the non-thrombus region through binarization of the image. The binarization step primarily involves setting a specific threshold value for black and white . As shown in Fig. 3d, the thrombus was expressed in white, and the non-thrombus region was expressed in black. This threshold-setting step involves subjective observations and decisions. The operator should carefully set the threshold so that any noticeable detail of the thrombus is not lost.
In the last step, the thrombus area was calculated from the ratio of the thrombus area to the total area of the binary image. This process was iteratively performed at each time interval to calculate the progress of thrombus formation over time.
Figure 4 shows the results of measuring the area of the thrombus formed in the channel with the stenosis percentages of 71.2% and 83.73% at 1-min intervals. In the case of the 71.2% channel, the thrombus growth rate changed after 7 min. This is based on the principle of thrombus formation, in which platelets are attached to the inner wall of the channel in the early stage of thrombus growth and aggregate to the attached platelets . In the case of the 83.73% channel, the thrombus grew faster than in the 71.2% channel case. However, no distinguishable slope change was observed between the platelet adhesion and aggregation phases.
The thrombus growth process is shown in Fig. 5. The progress of the 71.2% channel is shown in Fig. 5a. The thrombus formed slowly from the inner wall for up to 7 min. Thereafter, the thrombus gradually grew in size from the inner wall. The progress of the 83.73% channel is shown in Fig. 5b. The thrombus was generated on the channel wall for up to 1 min, and as soon as 1 min passed, it grew rapidly around the stenosis. It is expected that the channel with a higher stenosis percentage has a higher WSR; therefore, Reference  it would be easier for platelets to migrate to the channel wall, and platelet adhesion was predicted to occur as quickly. In comparison to the thrombus growth rates, the channel with 71.2% stenosis took 12 min to form 154,575.55 µm2 thrombus while that with 83.73% stenosis produced a similar area in 4 min. That is, the channel with 83.73% stenosis formed the thrombus approximately three times faster than that with 71.2% stenosis.
Moreover, it was confirmed that most of the thrombi formed in the channel with 71.2% stenosis were developed in the front part of the stenosis section, whereas the thrombus formed in the channel with 83.73% stenosis was mostly developed in the rear part of the stenosis section. The maximum WSR from the stenosis of each channel was 7600 s−1 for the channel with 71.2% stenosis and 29,000 s−1 for that with 83.3% stenosis, as presented in Table 1. This shows a trend similar to the simulation results of Yazdani et al.  that platelets aggregated in the front part of the stenosis section at shear rates of 2400–5400 s−1 and that they aggregated mainly at the rear part of the stenosis section at 11,000–21,000 s−1.
Notably, it was confirmed that the thrombus area was momentarily decreased at 3 and 7 min in the channel with 83.73% stenosis, unlike the channel with 71.2% stenosis, which was caused by the detachment of some thrombi because of the high WSR. A detached thrombus is referred to as an embolus. A high WSR increases the concentration of platelets near the channel walls and promotes platelet activation; conversely, it also separates the thrombus from the thrombus body . The thrombus formation process for 6–7 min during the interval in which the embolus occurred is shown in Fig. 6. It was visually confirmed that a part of the thrombus (embolus) that had already formed at 6 min had disappeared after 1 min.
The image-analysis-based thrombus visualization and area calculation methods used in this study may have lower accuracy as the analysis coverage area increases. Figure 7 shows the differences in the thrombus analysis when sectioning the area. Generally, if the width of an image is more than three times the height of the image, it is suggested that the analysis range is divided into sections, and each section is analyzed individually. The left side of Fig. 7 shows the thrombus image and the calculation result of the thrombus area when the analysis section is not divided, whereas the right side of Fig. 7 shows the thrombus image and the calculation of the thrombus area when the analysis section is divided. As a rule of thumb, each section should be divided such that the divided image width does not exceed three times the image height. In the case shown in Fig. 7, the total image was further divided every 500 µm so that the sections were divided into the normal channel zone (section 1, 6), the entry and departure zone (section 2, 5), and the stenosis zone (section 3, 4). Six sections were analyzed individually and compared with the case when the analysis section was not divided. We observed that the section-divided analysis showed more details, which was lost in the non-divided analysis. The reason for this difference was mainly the contrast resolution of each analyzed image. If the images were divided into sections, each divided image was individually calculated to obtain a range of grayscale values. The non-divided image was set to a grayscale range. Quantitatively, the analyzed thrombus area in Fig. 7 has approximately an 8% difference from the sectioning (531,325.075 vs. 489,191.296 µm2).
The individually analyzed image analysis results showed a region-specific thrombus growth process. Figure 8 shows the image analysis results for thrombus formation in the regions after the stenosis section (sections 4, 5, and 6), where most thrombi were formed. In sections 4 and 5, which are close to the stenosis section, the growth rate of the thrombus was the fastest in the early stages of blood perfusion. In contrast, in section 6, which is the farthest from the stenosis section, thrombus growth and detachment (embolus) occurred frequently. This means that the growth rate between the thrombus growing around the platelets attached to the channel wall (wall-platelet) is a faster and stronger bond than that between the thrombus formed during platelet aggregation (platelet-platelet). Additionally, 7 min after the blood flow, the thrombus area formed in section 5 reversed the thrombus area formed in section 4. It is expected that the WSR caused by the stenosis section, as well as the WSR caused by the thrombus formed in section 4, increased over time, thus promoting thrombus growth.
Since the thrombus analysis method introduced in this study is based on 2D projection images, it is difficult to accurately measure the absolute amount of thrombus formed. However, this method makes it possible to determine the location of the formed thrombus and compare the relative size of the thrombus. This provides a basis for observing and understanding the thrombus formation process.
In this study, a method for quantitatively analyzing the mechanism of thrombus formation in an in vitro thrombus chip was described using simple experimental equipment and image analysis without using fluorescent materials. This study provided a basic step in enabling thrombus analysis to understand and treat thrombus disorders using simple laboratory equipment. The thrombus analysis results shown in this study were mainly based on the influence of physical flow on thrombosis formation. Through the image analysis method introduced in this study, it was possible to visually monitor the thrombus formation process over time and calculate the thrombus area formed. We found that the higher the stenosis channel, the faster the thrombus growth rate, and the higher the probability of embolus occurrence. It was also shown that thrombus formation started from the channel wall and grew around it. Furthermore, by dividing the image analysis section and analyzing it by section, it was confirmed that the growth rate and binding force of the same thrombus can vary depending on how they were formed. We believe that our results provide a basis for the fabrication of in vitro thrombus kits for POC diagnosis of arterial thrombosis and rapid diagnosis of antithrombotic therapy.
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
Wall shear rate
Point of care
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Palasubramaniam J, Wang X, Peter K (2019) Myocardial infarction—from atherosclerosis to thrombosis: uncovering new diagnostic and therapeutic approaches. Arterioscler Thromb Vasc Biol 39(8):E176–E185. https://doi.org/10.1161/ATVBAHA.119.312578
Pratt DN, Diez A (2018) Kidney in heart failure. In: Encyclopedia of cardiovascular research and medicine. Elsevier, Amsterdam, pp 155–165. https://doi.org/10.1016/b978-0-12-809657-4.11045-2
Sakariassen KS, Orning L, Turitto VT (2015) The impact of blood shear rate on arterial thrombus formation. Future Sci. https://doi.org/10.4155/fso.15.28
Muthard RW, Diamond SL (2013) Side view thrombosis microfluidic device with controllable wall shear rate and transthrombus pressure gradient. Lab Chip 13(10):1883–1891. https://doi.org/10.1039/c3lc41332b
Bark DL, Ku DN (2010) Wall shear over high degree stenoses pertinent to atherothrombosis. J Biomech 43(15):2970–2977. https://doi.org/10.1016/j.jbiomech.2010.07.011
Alvaro M, et al (1994) Synergistic action of severe wall injury and shear forces on thrombus formation in arterial stenosis definition of a thrombotic shear rate threshold. J Am Coll Cardiol 24(4):1091–1097. https://doi.org/10.1016/0735-1097(94)90875-3
Claesson K, Lindahl TL, Faxälv L (2016) Counting the platelets: a robust and sensitive quantification method for thrombus formation. Thromb Haemost 115(6):1178–1190. https://doi.org/10.1160/TH15-10-0799
Aghayee S, Benadiba C, Notz J, Kasas S, Dietler G, Longo G (2013) Combination of fluorescence microscopy and nanomotion detection to characterize bacteria. J Mol Recognit 26(11):590–595. https://doi.org/10.1002/jmr.2306
Vaidya B, Nayak MK, Dash D, Agrawal GP, Vyas SP (2016) Development and characterization of highly selective target-sensitive liposomes for the delivery of streptokinase: in vitro/in vivo studies. Drug Deliv 23(3):801–807. https://doi.org/10.3109/10717544.2014.916770
Deitcher SR, Carman TL, Kottke-Marchant K (2002) Simultaneous deep venous thrombosis and acquired factor VIII inhibitor
Nguyen TQ, Park WT (2016) Rapid, low-cost fabrication of circular microchannels by air expansion into partially cured polymer. Sens Actuators B Chem 235:302–308. https://doi.org/10.1016/j.snb.2016.05.008
Nguyen TQ, Park WT (2020) Fabrication method of multi-depth circular microchannels for investigating arterial thrombosis-on-a-chip. Sens Actuators B Chem. https://doi.org/10.1016/j.snb.2020.128590
Pollet AMAO, Homburg EFGA, Cardinaels R, den Toonder JMJ (2020) 3D sugar printing of networks mimicking the vasculature. Micromachines (Basel). https://doi.org/10.3390/mi11010043
Lui M, et al. Novel stenotic microchannels to study thrombus formation in shear gradients: influence of shear forces and human platelet-related factors. Int J Mol Sci. https://doi.org/10.3390/ijms20122967
Casa LDC, Ku DN, Woodruff GW (2017) Thrombus formation at high shear rates. https://doi.org/10.1146/annurev-bioeng-071516
Li M, Ku DN, Forest CR (2012) Microfluidic system for simultaneous optical measurement of platelet aggregation at multiple shear rates in whole blood. Lab Chip 12(7):1355–1362. https://doi.org/10.1039/c2lc21145a
Turitto VT, Weiss HJ, Baumgartners HR. The effect of shear rate on platelet interaction with subendothelium exposed to citrated human blood
Maxwell MJ et al (2006) Shear induces a unique series of morphological changes in translocating platelets. Arterioscler Thromb Vasc Biol 26(3):663–669. https://doi.org/10.1161/01.ATV.0000201931.16535.e1
Singh S et al (2019) Structure functional insights into calcium binding during the activation of coagulation factor XIII A. Sci Rep 9(1):1–18. https://doi.org/10.1038/s41598-019-47815-z
Shi X et al (2016) Effects of different shear rates on the attachment and detachment of platelet thrombi. Mol Med Rep 13(3):2447–2456. https://doi.org/10.3892/mmr.2016.4825
Grande JC (2012) Principles of image analysis. Metallogr Microstruct Anal 1:227–243. https://doi.org/10.1007/s13632-012-0037-5
Hartig SM (2013) Basic image analysis and manipulation in imageJ. Curr Protoc Mol Biol. https://doi.org/10.1002/0471142727.mb1415s102
Govindarajan V, Rakesh V, Reifman J, Mitrophanov AY (2016) Computational study of thrombus formation and clotting factor effects under venous flow conditions. Biophys J 110(8):1869–1885. https://doi.org/10.1016/j.bpj.2016.03.010
Yazdani A, Li H, Humphrey JD, Karniadakis GE (2017) A general shear-dependent model for thrombus formation. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1005291
This study was conducted with the support of the National Research Foundation of Korea (2020R1F1A107499512) and was conducted after approval by the Seoul National University of Science and Technology IRB (2020-0025-01).
The authors declare that they have no competing interests.
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Choi, JS., Ham, DH., Kim, JH. et al. Quantitative image analysis of thrombus formation in microfluidic in-vitro models. Micro and Nano Syst Lett 10, 23 (2022). https://doi.org/10.1186/s40486-022-00166-3
- Thrombus formation
- Image analysis